Case Study - Matching candidates to roles with machine learning at scale
A technical-recruitment platform needed to move beyond keyword matching to genuine candidate-to-role intelligence across large, unstructured datasets. We built it on Next.js and a fully serverless AWS backend.
- Sector
- HR Technology & Recruitment
- Year
- Service
- AI Apps & Cloud Engineering
Overview
The client ran a recruitment platform focused on technical hiring. Their existing matching relied on keyword overlap, which missed strong candidates and surfaced weak ones. They needed a system that understood the substance of a role and a résumé, not just the words they shared.
The challenge
Candidate data arrived in many shapes and was largely unstructured. Matching had to happen across a large and growing dataset, fast enough to support live shortlisting, and in a way the team could trust and explain. The old approach did none of these well.
What we built
- Next.js client
- AWS Lambda
- Amazon S3
- NoSQL data layer
- ML ranking & embeddings
We built the interface on Next.js and a fully serverless backend on AWS — Lambda for compute, S3 for document and asset storage, and a NoSQL store for flexible candidate schemas. Machine-learning models generate embeddings for roles and candidates and drive ranking, so the platform surfaces strong matches in seconds rather than through manual triage.
The serverless design means matching capacity scales with demand and costs track usage, rather than sitting idle between hiring pushes.
The outcome
The platform moved from keyword overlap to genuine semantic matching, cutting the manual triage burden and raising the quality of shortlists.
Detailed performance figures for this engagement are available on request, under mutual NDA.